arXiv:2605.30628v1 Announce Type: cross Abstract: Universal LLM reliability is not a finite-library problem: across all possible tasks, tools, schemas, knowledge sources, and evaluator expectations, new intervention-distinguishable failure modes can appear without bound, so no finite intervention dictionary can guarantee bounded residual error for every such mode. But deployed systems do not operate over the whole universe. They operate inside operationally bounded patches (legal review, medical RAG, code repair, customer-support agents, contract extraction) with recurring tasks, schemas, tool
This paper highlights a critical challenge for the rapid deployment of AI systems, particularly LLMs, as real-world applications expose the limitations of universal reliability approaches.
It provides a more realistic framework for achieving reliable LLMs within bounded operational contexts, which is crucial for enterprises and governments seeking to integrate AI into critical functions.
The focus shifts from seeking universal LLM reliability to understanding and managing 'patch-local' or domain-specific reliability through targeted interventions and finite-library solutions.
- · AI Safety Researchers
- · Domain-Specific LLM Developers
- · Enterprises Adopting LLMs for Specific Tasks
- · Platforms Promising Universal LLM Solutions
- · Organizations Seeking 'One-Size-Fits-All' AI
Increased investment in explainable AI and reliability engineering tailored to specific application domains.
Development of specialized LLM 'patches' and intervention dictionaries becoming a new AI service market.
Enhanced regulatory scrutiny on highly generalized LLM deployments without clear operational bounds and reliability guarantees.
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Read at arXiv cs.LG